GLP-1 receptor agonists synergize with DYRK1A inhibitors to potentiate functional human β cell regeneration
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor (GLP1R) agonists and dipeptidyl peptidase 4 inhibitors are widely prescribed diabetes drugs due to their ability to stimulate insulin secretion from remaining β cells and to reduce caloric intake. Unfortunately, they fail to increase human β cell proliferation. Small-molecule inhibitors of dual-specificity tyrosine-regulated kinase 1A (DYRK1A) are able to induce adult human β cell proliferation, but rates are modest (~2%), and their specificity to β cells is limited. Here, we provide evidence that combining any member of the GLP1R agonist class with any member of the DYRK1A inhibitor class induces a synergistic increase in human β cell replication (5 to 6%) accompanied by an actual increase in numbers of human β cells. GLP1R agonist-DYRK1A inhibitor synergy required combined inhibition of DYRK1A and an increase in cAMP and did not lead to β cell dedifferentiation. These beneficial effects on proliferation were seen in both normal human β cells and β cells derived from individuals with type 2 diabetes. The ability of the GLP1R agonist-DYRK1A inhibitor combination to enhance human β cell proliferation, human insulin secretion, and blood glucose control extended in vivo to studies of human islets transplanted into euglycemic and streptozotocin-diabetic immunodeficient mice. No adverse events were observed in the mouse studies during a 1-week period. Because of the relative β cell specificity of GLP1R agonists, the combination provides an improved, although not complete, degree of human β cell specificity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".